Abstract Hardware-implemented machine learning algorithms are finding their way in various domains, including safety-critical applications. This has demanded these algorithms to perform correctly even in harsh environmental conditions, such as in avionics altitudes. Support Vector Machine (SVM) is an important Machine Learning that has been target of hardware implementation in recent years. This is the first work to asses both Binary and Multiclass SVMs under thermal neutron radiation, a type of particle noticeably present in high altitudes. A fault injection campaign along with a radiation test with the D50 thermal neutron source, at the Intitut Laue-Languevin, has been performed. The results show a high intrinsic fault tolerance for both varieties of the SVM algorithm, especially for the Multiclass SVM.

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热中子辐射对硬件实现的机器学习算法的影响

摘要 硬件实现的机器学习算法正在各个领域找到自己的方法，包括安全关键应用程序。这要求这些算法即使在恶劣的环境条件下也能正确执行，例如在航空电子设备的高度。支持向量机（SVM）是一种重要的机器学习，近年来一直是硬件实现的目标。这是在热中子辐射下评估二元和多类 SVM 的第一项工作，热中子辐射是一种明显存在于高空的粒子。已经在劳厄-朗格万研究所进行了故障注入活动以及 D50 热中子源的辐射测试。结果表明，两种 SVM 算法都具有很高的内在容错能力，尤其是多类 SVM。